Replacing Pandas or Numpy Nan with a None to use with MysqlDB

I am trying to write a Pandas dataframe (or can use a numpy array) to a mysql database using MysqlDB . MysqlDB doesn't seem understand 'nan' and my database throws out an error saying nan is not in the field list. I need to find a way to convert the 'nan' into a NoneType.

Any ideas?

@bogatron has it right, you can use where, it's worth noting that you can do this natively in pandas:

df1 = df.where(pd.notnull(df), None)

Note: this changes the dtype of all columns to object.

Example:

In [1]: df = pd.DataFrame([1, np.nan])

In [2]: df
Out[2]: 
    0
0   1
1 NaN

In [3]: df1 = df.where(pd.notnull(df), None)

In [4]: df1
Out[4]: 
      0
0     1
1  None

---

Note: what you cannot do recast the DataFrames dtype to allow all datatypes types, using astype, and then the DataFrame fillna method:

df1 = df.astype(object).replace(np.nan, 'None')

Unfortunately neither this, nor using replace, works with None see this (closed) issue.

---

As an aside, it's worth noting that for most use cases you don't need to replace NaN with None, see this question about the difference between NaN and None in pandas.

However, in this specific case it seems you do (at least at the time of this answer).

df = df.replace({np.nan: None})

Note: this changes the dtype of all affected columns to object.

Credit goes to this guy here on this Github issue.

You can replace nan with None in your numpy array:

>>> x = np.array([1, np.nan, 3])
>>> y = np.where(np.isnan(x), None, x)
>>> print y
[1.0 None 3.0]
>>> print type(y[1])
<type 'NoneType'>

After stumbling around, this worked for me:

df = df.astype(object).where(pd.notnull(df),None)

Another addition: be careful when replacing multiples and converting the type of the column back from object to float. If you want to be certain that your None's won't flip back to np.NaN's apply @andy-hayden's suggestion with using pd.where. Illustration of how replace can still go 'wrong':

In [1]: import pandas as pd

In [2]: import numpy as np

In [3]: df = pd.DataFrame({"a": [1, np.NAN, np.inf]})

In [4]: df
Out[4]:
     a
0  1.0
1  NaN
2  inf

In [5]: df.replace({np.NAN: None})
Out[5]:
      a
0     1
1  None
2   inf

In [6]: df.replace({np.NAN: None, np.inf: None})
Out[6]:
     a
0  1.0
1  NaN
2  NaN

In [7]: df.where((pd.notnull(df)), None).replace({np.inf: None})
Out[7]:
     a
0  1.0
1  NaN
2  NaN

Just an addition to @Andy Hayden's answer:

Since DataFrame.mask is the opposite twin of DataFrame.where, they have the exactly same signature but with opposite meaning:

• DataFrame.where is useful for Replacing values where the condition is False.
• DataFrame.mask is used for Replacing values where the condition is True.

So in this question, using df.mask(df.isna(), other=None, inplace=True) might be more intuitive.

replace np.nan with None is accomplished differently across different version of pandas:

if version.parse(pd.__version__) >= version.parse('1.3.0'):
    df = df.replace({np.nan: None})
else:
    df = df.where(pd.notnull(df), None)

this solves the issue that for pandas versions <1.3.0, if the values in df are already None then df.replace({np.nan: None}) will toggle them back to np.nan (and vice versa).

Quite old, yet I stumbled upon the very same issue. Try doing this:

df['col_replaced'] = df['col_with_npnans'].apply(lambda x: None if np.isnan(x) else x)

I believe the cleanest way would be to make use of the na_value argument in the pandas.DataFrame.to_numpy() method (docs):

> na_value : Any, optional > > The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns. > > New in version 1.1.0.

You could e.g. convert to dictionaries with NaN's replaced by None using

columns = df.columns.tolist()
dicts_with_nan_replaced = [
    dict(zip(columns, x))
    for x in df.to_numpy(na_value=None)
]

Convert numpy NaN to pandas NA before replacing with the where statement:

df = df.replace(np.NaN, pd.NA).where(df.notnull(), None)

Do you have a code block to review by chance?

Using .loc, pandas can access records based on logic conditions (filtering) and do action with them (when using =). Setting a .loc mask equal to some value will change the return array inplace (so be a touch careful here; I suggest test on a df copy prior to using in code block).

df.loc[df['SomeColumn'].isna(), 'SomeColumn'] = None

The outer function is df.loc[row_label, column_label] = None. We're going to use a boolean mask for row_label by using the .isna() method to find 'NoneType' values in our column SomeColumn.

We'll use the .isna() method to return a boolean array of rows/records in column SomeColumn as our row_label: df['SomeColumn'].isna(). It will isolate all rows where SomeColumn has any of the 'NoneType' items pandas checks for with the .isna() method.

We'll use the column_label both when masking the dataframe for the row_label, and to identify the column we want to act on for the .loc mask.

Finally, we set the .loc mask equal to None, so the rows/records returned are changed to None based on the masked index.

Below are links to pandas documentation regarding .loc & .isna().

References:
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isna.html

After finding that neither the recommended answer, nor the alternate suggested worked for my application after a Pandas update to 1.3.2 I settled for safety with a brute force approach:

buf = df.to_json(orient='records')
recs = json.loads(buf)

Yet another option, that actually did the trick for me:

df = df.astype(object).replace(np.nan, None)

Astoundingly, None of the previous answers worked for me, so I had to do it for each column.

for column in df.columns:
            df[column] = df[column].where(pd.notnull(df[column]), None)

This worked for me:

df = df.fillna(0)